Introduction to Computer Science and Programming (Spring 2011) (M-I-T)

Lecture 10: Hashing and Classes (M-I-T)

Lecture 11: Object Oriented Programming (OOP) and Inheritance (M-I-T)

Lecture 12: Introduction to Simulation and Random Walks (M-I-T)

Lecture 13: Some Basic Probability and Plotting Data (M-I-T)

Lecture 14: Sampling and Monte Carlo Simulation (M-I-T)

Lecture 15: Statistical Thinking (M-I-T)

Lecture 16: Using Randomness to Solve Non-random Problems (M-I-T)

Lecture 17: Curve Fitting (M-I-T)

Lecture 18: Optimization Problems and Algorithms (M-I-T)

Lecture 19: More Optimization and Clustering (M-I-T)

Lecture 1: Introduction to Computer Science and Programming (M-I-T)

Lecture 20: More Clustering (M-I-T)

Lecture 21: Using Graphs to Model Problems, Part 1 (M-I-T)

Lecture 22: Using Graphs to Model Problems, Part 2 (M-I-T)

Lecture 23: Dynamic Programming (M-I-T)

Lecture 24: Avoiding Statistical Fallacies (M-I-T)

Lecture 25: Queuing Network Models (M-I-T)

Lecture 26: What Do Computer Scientists Do? (M-I-T)

Lecture 2: Core Elements of a Program (M-I-T)

Lecture 3: Problem Solving (M-I-T)

Lecture 4: Machine Interpretation of a Program (M-I-T)

Lecture 5: Objects in Python (M-I-T)

Lecture 6: Recursion (M-I-T)

Lecture 9: Memory and Search Methods (M-I-T)

Lecture 7: Debugging (M-I-T)